# Copyright 2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""" Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type

from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit

# PyTorch has an optimized, native 'silu' (aka 'swish') operator as of PyTorch 1.7.
# Also hardsigmoid, hardswish, and soon mish. This code will use native version if present.
# Eventually, the custom SiLU, Mish, Hard*, layers will be removed and only native variants will be used.
_has_silu = 'silu' in dir(torch.nn.functional)
_has_hardswish = 'hardswish' in dir(torch.nn.functional)
_has_hardsigmoid = 'hardsigmoid' in dir(torch.nn.functional)
_has_mish = 'mish' in dir(torch.nn.functional)

from torch import Tensor
import torch_npu
class NpuSiLU(nn.Module):
    __constants__ = ['inplace']
    inplace: bool

    def __init__(self, inplace: bool = False):
        super(NpuSiLU, self).__init__()
        self.inplace = inplace

    def forward(self, input: Tensor) -> Tensor:
        return torch_npu.npu_silu(input)

    def extra_repr(self) -> str:
        inplace_str = 'inplace=True' if self.inplace else ''
        return inplace_str

nn.SiLU = NpuSiLU

_ACT_FN_DEFAULT = dict(
    silu=F.silu if _has_silu else swish,
    swish=F.silu if _has_silu else swish,
    mish=F.mish if _has_mish else mish,
    relu=F.relu,
    relu6=F.relu6,
    leaky_relu=F.leaky_relu,
    elu=F.elu,
    celu=F.celu,
    selu=F.selu,
    gelu=gelu,
    gelu_tanh=gelu_tanh,
    quick_gelu=quick_gelu,
    sigmoid=sigmoid,
    tanh=tanh,
    hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid,
    hard_swish=F.hardswish if _has_hardswish else hard_swish,
    hard_mish=hard_mish,
)

_ACT_FN_JIT = dict(
    silu=F.silu if _has_silu else swish_jit,
    swish=F.silu if _has_silu else swish_jit,
    mish=F.mish if _has_mish else mish_jit,
    hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_jit,
    hard_swish=F.hardswish if _has_hardswish else hard_swish_jit,
    hard_mish=hard_mish_jit,
)

_ACT_FN_ME = dict(
    silu=F.silu if _has_silu else swish_me,
    swish=F.silu if _has_silu else swish_me,
    mish=F.mish if _has_mish else mish_me,
    hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_me,
    hard_swish=F.hardswish if _has_hardswish else hard_swish_me,
    hard_mish=hard_mish_me,
)

_ACT_FNS = (_ACT_FN_ME, _ACT_FN_JIT, _ACT_FN_DEFAULT)
for a in _ACT_FNS:
    a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
    a.setdefault('hardswish', a.get('hard_swish'))


_ACT_LAYER_DEFAULT = dict(
    silu=nn.SiLU if _has_silu else Swish,
    swish=nn.SiLU if _has_silu else Swish,
    mish=nn.Mish if _has_mish else Mish,
    relu=nn.ReLU,
    relu6=nn.ReLU6,
    leaky_relu=nn.LeakyReLU,
    elu=nn.ELU,
    prelu=PReLU,
    celu=nn.CELU,
    selu=nn.SELU,
    gelu=GELU,
    gelu_tanh=GELUTanh,
    quick_gelu=QuickGELU,
    sigmoid=Sigmoid,
    tanh=Tanh,
    hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoid,
    hard_swish=nn.Hardswish if _has_hardswish else HardSwish,
    hard_mish=HardMish,
    identity=nn.Identity,
)

_ACT_LAYER_JIT = dict(
    silu=nn.SiLU if _has_silu else SwishJit,
    swish=nn.SiLU if _has_silu else SwishJit,
    mish=nn.Mish if _has_mish else MishJit,
    hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidJit,
    hard_swish=nn.Hardswish if _has_hardswish else HardSwishJit,
    hard_mish=HardMishJit,
)

_ACT_LAYER_ME = dict(
    silu=nn.SiLU if _has_silu else SwishMe,
    swish=nn.SiLU if _has_silu else SwishMe,
    mish=nn.Mish if _has_mish else MishMe,
    hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidMe,
    hard_swish=nn.Hardswish if _has_hardswish else HardSwishMe,
    hard_mish=HardMishMe,
)

_ACT_LAYERS = (_ACT_LAYER_ME, _ACT_LAYER_JIT, _ACT_LAYER_DEFAULT)
for a in _ACT_LAYERS:
    a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
    a.setdefault('hardswish', a.get('hard_swish'))


def get_act_fn(name: Union[Callable, str] = 'relu'):
    """ Activation Function Factory
    Fetching activation fns by name with this function allows export or torch script friendly
    functions to be returned dynamically based on current config.
    """
    if not name:
        return None
    if isinstance(name, Callable):
        return name
    if not (is_no_jit() or is_exportable() or is_scriptable()):
        # If not exporting or scripting the model, first look for a memory-efficient version with
        # custom autograd, then fallback
        if name in _ACT_FN_ME:
            return _ACT_FN_ME[name]
    if not (is_no_jit() or is_exportable()):
        if name in _ACT_FN_JIT:
            return _ACT_FN_JIT[name]
    return _ACT_FN_DEFAULT[name]


def get_act_layer(name: Union[Type[nn.Module], str] = 'relu'):
    """ Activation Layer Factory
    Fetching activation layers by name with this function allows export or torch script friendly
    functions to be returned dynamically based on current config.
    """
    if name is None:
        return None
    if not isinstance(name, str):
        # callable, module, etc
        return name
    if not name:
        return None
    if not (is_no_jit() or is_exportable() or is_scriptable()):
        if name in _ACT_LAYER_ME:
            return _ACT_LAYER_ME[name]
    if not (is_no_jit() or is_exportable()):
        if name in _ACT_LAYER_JIT:
            return _ACT_LAYER_JIT[name]
    return _ACT_LAYER_DEFAULT[name]


def create_act_layer(name: Union[Type[nn.Module], str], inplace=None, **kwargs):
    act_layer = get_act_layer(name)
    if act_layer is None:
        return None
    if inplace is None:
        return act_layer(**kwargs)
    try:
        return act_layer(inplace=inplace, **kwargs)
    except TypeError:
        # recover if act layer doesn't have inplace arg
        return act_layer(**kwargs)
